knitr::opts_chunk$set(message = FALSE)
knitr::opts_chunk$set(warning = FALSE)
library(ggplot2)
library(lme4)
library(WRS2)
library(knitr)
library(reshape2)
library(plyr)
library(dplyr)
library(stringr)
library(sjPlot)
library(cowplot)
library(fmsb)
library(ggthemes)
library(effects)
library(grid)
library(gridExtra)
#library(magick)
library(data.table)
### FLANKER ###
# set working directory - where R will look for our data
setwd("/Volumes/EEG/CategoryLearning/Data/Exp2/Flanker")
# create a list of file names for R to search through when compiling data
file_names_Flanker <- dir(pattern = "flanker-[0-9+].*.csv", recursive=TRUE)
# create an empty data frame into which the compiled data will be entered
raw_Flanker <- data.frame(matrix(vector(), 0, 11,
dimnames=list(c(),
c("subnum", "block", "trial", "practice", "targdir", "flankercoherence",
"flankerdir", "time0", "resp", "corr", "rt"))),
stringsAsFactors=FALSE)
# for each file in our file names structure, add it to an empty data frame
# and bind to the raw empty data frame produced above
for (i in file_names_Flanker) {
data_Flanker <- fread(i, header = TRUE, sep = ",", na.strings="NR")
data_Flanker <- `colnames<-`(data_Flanker, c("subnum", "block", "trial", "practice", "targdir", "flankercoherence",
"flankerdir", "time0", "resp", "corr", "rt"))
raw_Flanker <- rbind(raw_Flanker, data_Flanker)
}
### TOL ###
# set working directory - where R will look for our data
setwd("/Volumes/EEG/CategoryLearning/Data/Exp2/ToL")
# create a list of file names for R to search through when compiling data
file_names_TOL <- dir(pattern = "tol-summary-[0-9+].*.csv", recursive=TRUE)
# create an empty data frame into which the compiled data will be entered
raw_TOL <- data.frame(matrix(vector(), 0, 16,
dimnames=list(c(),
c("sub", "trial", "size", "shortest", "startlab", "endlab",
"startconf", "endconf", "success", "tries", "score", "steps",
"roundstart", "starttime", "firsttime", "time"))),
stringsAsFactors=FALSE)
# for each file in our file names structure, add it to an empty data frame
# and bind to the raw empty data frame produced above
for (i in file_names_TOL) {
data_TOL <- fread(i, header = TRUE, sep = ",", na.strings="NR")
data_TOL <- `colnames<-`(data_TOL, c("sub", "trial", "size", "shortest", "startlab", "endlab",
"startconf", "endconf", "success", "tries", "score", "steps",
"roundstart", "starttime", "firsttime", "time"))
raw_TOL <- rbind(raw_TOL, data_TOL)
}
### SWITCHER ###
# set working directory - where R will look for our data
setwd("/Volumes/EEG/CategoryLearning/Data/Exp2/Switcher")
# create a list of file names for R to search through when compiling data
file_names_Switcher <- dir(pattern = "switch-summary-[0-9+].*.csv", recursive=TRUE)
# create an empty data frame into which the compiled data will be entered
raw_Switcher <- data.frame(matrix(vector(), 0, 11,
dimnames=list(c(),
c("subNum", "timestamp", "testtype", "numcues", "numerr",
"matchlast", "matchany", "tooslow", "waittime", "perftime", "medtime"))),
stringsAsFactors=FALSE)
# for each file in our file names structure, add it to an empty data frame
# and bind to the raw empty data frame produced above
for (i in file_names_Switcher) {
data_Switcher <- fread(i, header = TRUE, sep = ",", na.strings="NR")
data_Switcher <- `colnames<-`(data_Switcher, c("subNum", "timestamp", "testtype", "numcues", "numerr",
"matchlast", "matchany", "tooslow", "waittime", "perftime", "medtime"))
raw_Switcher <- rbind(raw_Switcher, data_Switcher)
}
### FLANKER ###
#raw_Flanker produces a data frame wherein...
# targdir = -1 means left; +1 means right
# flankerdir = same coding
# flankercoherence = -1 means mismatch; 0 means neutral, +1 means match
## SUMMARIZING ACCURACY AND RT ##
#remove practice trials
data_Flanker <- subset(raw_Flanker, practice==0)
## QUICK CLEAN
# fix subnums
data_Flanker$subnum[data_Flanker$subnum==1] <- 8076
data_Flanker$subnum[data_Flanker$subnum==7] <- 8001
data_Flanker$subnum[data_Flanker$subnum==9] <- 8002
data_Flanker$subnum[data_Flanker$subnum==4] <- 8003
data_Flanker$subnum[data_Flanker$subnum==11] <- 8114
data_Flanker$subnum[data_Flanker$subnum==21] <- 8021
data_Flanker$subnum[data_Flanker$subnum==29] <- 8036
data_Flanker$subnum[data_Flanker$subnum==44] <- 8054
data_Flanker$subnum[data_Flanker$subnum==50] <- 8064
data_Flanker$subnum[data_Flanker$subnum==84] <- 8124
data_Flanker$subnum[data_Flanker$subnum==86] <- 8125
data_Flanker$subnum[data_Flanker$subnum==87] <- 8126
#remoove non-responses
data_Flanker <- subset(data_Flanker, resp=="<lshift>" | resp=="<rshift>")
#summarize data by targdir and flankercoherence
data_Flanker_sum <- with(data_Flanker, aggregate(list(corr, rt), list(flankercoherence),
mean, na.rm=TRUE))
colnames(data_Flanker_sum) <- c("flankerCoherence", "acc", "rt")
kable(data_Flanker_sum, format="html")
|
flankerCoherence
|
acc
|
rt
|
|
-1
|
0.8867080
|
464.9551
|
|
0
|
0.9822616
|
401.1182
|
|
1
|
0.9767958
|
418.2730
|
|
NA
|
0.9656352
|
423.6189
|
data_Flanker_sub <- with(data_Flanker, aggregate(list(corr, rt), list(subnum, flankercoherence),
mean, na.rm=TRUE))
colnames(data_Flanker_sub) <- c("subject", "flankerCoherence", "acc", "rt")
data_Flanker_sub.1 <- subset(data_Flanker_sub, flankerCoherence==-1)
hist(data_Flanker_sub.1$acc)
hist(data_Flanker_sub.1$rt)
## PROCESSING AND CALCULATING FLANKER EFFECTS
#remove incorrect responses
data_Flanker_cor <- subset(data_Flanker, corr==1)
#subset for only coherent and incoherent trials
data_Flanker_trim <- subset(data_Flanker_cor, flankercoherence=="-1" | flankercoherence=="1")
data_Flanker_trim$flankercoherence <- ifelse(data_Flanker_trim$flankercoherence=="1", "flank_Congruent", "flank_Incongruent")
#reduce to by-subject data-frame
data_Flanker_trim_sub <- with(data_Flanker_trim, aggregate(list(rt), list(subnum, flankercoherence),
mean, na.rm=TRUE))
colnames(data_Flanker_trim_sub) <- c("subject", "flank_Coherence", "flank_rt")
#melt and cast for calculating flanker effect
data_Flanker_trim_melt <- melt(data_Flanker_trim_sub, id.vars=c("subject", "flank_Coherence"))
data_Flanker_trim_cast <- dcast(data_Flanker_trim_melt, subject ~ flank_Coherence)
data_Flanker_trim_cast$flank_eff <- data_Flanker_trim_cast$flank_Incongruent - data_Flanker_trim_cast$flank_Congruent
hist(data_Flanker_trim_cast$flank_eff)
### TOL ###
#sub indicates the subject code
#trial indicates which trial in the test
#size is the number of disks in the problem
#shortest is the least number of moves needed to solve the problem (may not be available in all tests)
#startlab is a node within an internal matrix indicating the starting configuration
#endlab is a nod within an internal matrix indicating ending configuration.
#startconf is the configuration of disks at the beginning
#endconf is the configuration at the end.
#success is whether the participant completed the task (may be 0 when a time or move limit is used)
#tries is how many attempts were made (how many times it was reset, not used in all versions)
#score is a running total score, which is just one point per number of disks in correctly solved problems
#steps is the number of moves used to solve the problem
#roundstart is the absolute timer time when the problem was given (in ms)
#starttime is a second measure of this that resets if multiple attempts are given
#firstime is the first move time in ms
#time is the total time for the problem in ms, including first move time.
## SUMMARIZE DATA BY SUBJECT AND TRIAL ##
#exclude subject 9, who for some reasoon completed 30 trials of TOL...
data_TOL <- subset(raw_TOL, sub != 9 & sub != 8057)
#rename
data_TOL$sub[data_TOL$sub==1] <- 8076
data_TOL$sub[data_TOL$sub==7] <- 8001
data_TOL$sub[data_TOL$sub==9] <- 8002
data_TOL$sub[data_TOL$sub==4] <- 8003
data_TOL$sub[data_TOL$sub==11] <- 8114
data_TOL$sub[data_TOL$sub==21] <- 8021
data_TOL$sub[data_TOL$sub==29] <- 8036
data_TOL$sub[data_TOL$sub==44] <- 8054
data_TOL$sub[data_TOL$sub==50] <- 8064
data_TOL$sub[data_TOL$sub==84] <- 8124
data_TOL$sub[data_TOL$sub==86] <- 8125
data_TOL$sub[data_TOL$sub==87] <- 8126
#collapse data for individual subject data
data_TOL_sub <- with(data_TOL, aggregate(list(success, time), list(sub),
mean, na.rm=TRUE))
colnames(data_TOL_sub) <- c("subject", "TOL_success", "TOL_time")
hist(data_TOL_sub$TOL_success)
hist(data_TOL_sub$TOL_time)
#examine first-trial data only
data_TOL_sub.1 <- with(data_TOL, aggregate(list(success, time), list(sub, trial),
mean, na.rm=TRUE))
colnames(data_TOL_sub.1) <- c("subject", "TOL_trial", "TOL_success", "TOL_time")
data_TOL_sub.1 <- subset(data_TOL_sub.1, TOL_trial==1)
#collapse data for trial-level data
data_TOL_trial <- with(data_TOL, aggregate(list(success, firsttime, time), list(trial),
mean, na.rm=TRUE))
colnames(data_TOL_trial) <- c("TOL_trial", "TOL_success", "TOL_first", "TOL_time")
data_TOL_move <- with(data_TOL, aggregate(list(success, time), list(shortest),
mean, na.rm=TRUE))
colnames(data_TOL_move) <- c("TOL_minMoves", "TOL_success", "TOL_time")
#collapse data to overall success rate
data_TOL_success <- with(data_TOL, aggregate(list(success, firsttime, time), list(sub),
mean, na.rm=TRUE))
colnames(data_TOL_success) <- c("subject", "TOL_acc", "TOL_first", "TOL_time")
hist(data_TOL_success$TOL_acc)
#plot data as a function of successes and RTs (a) over time (trials) and (b) over min number of moves
TOLtrialPlot.success <- ggplot(data_TOL_trial, aes(x=TOL_trial, y=TOL_success)) +
geom_point() +
geom_line() +
scale_x_continuous(name="trial", breaks=c(1:12)) +
ggtitle("Success by trial") +
ylab("success") +
theme_few()
TOLtrialPlot.success
TOLtrialPlot.time <- ggplot(data_TOL_trial, aes(x=TOL_trial, y=TOL_time)) +
geom_point() +
geom_line() +
scale_x_continuous(name="trial", breaks=c(1:12)) +
ggtitle("Response time by trial") +
ylab("response time") +
theme_few()
TOLtrialPlot.time
TOLtrialPlot.first <- ggplot(data_TOL_trial, aes(x=TOL_trial, y=TOL_first)) +
geom_point() +
geom_line() +
scale_x_continuous(name="trial", breaks=c(1:12)) +
ggtitle("Response time by trial") +
ylab("time to first move") +
theme_few()
TOLtrialPlot.first
TOLmovePlot.success <- ggplot(data_TOL_move, aes(x=TOL_minMoves, y=TOL_success)) +
geom_point() +
geom_line() +
scale_x_continuous(name="moves", breaks=c(1:12)) +
ggtitle("Success by minimum number of moves (i.e., difficulty)") +
ylab("success") +
theme_few()
TOLmovePlot.success
TOLmovePlot.time <- ggplot(data_TOL_move, aes(x=TOL_minMoves, y=TOL_time)) +
geom_point() +
geom_line() +
scale_x_continuous(name="moves", breaks=c(1:12)) +
ggtitle("Response time by minimum number of moves (i.e., difficulty)") +
ylab("response time") +
theme_few()
TOLmovePlot.time
## ANALYZE DATA FOR MIN MOVES, ETC ##
successMod <- glmer(success ~ (1|sub), data_TOL, binomial)
summary(successMod)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: success ~ (1 | sub)
## Data: data_TOL
##
## AIC BIC logLik deviance df.resid
## 2375.2 2386.2 -1185.6 2371.2 1846
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8270 -1.0864 0.6007 0.7168 1.2782
##
## Random effects:
## Groups Name Variance Std.Dev.
## sub (Intercept) 0.3187 0.5646
## Number of obs: 1848, groups: sub, 153
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.64242 0.06861 9.363 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
successMod.1 <- glmer(success ~ as.factor(shortest) + (1|sub), data_TOL, binomial)
summary(successMod.1)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: success ~ as.factor(shortest) + (1 | sub)
## Data: data_TOL
##
## AIC BIC logLik deviance df.resid
## 2281.4 2309.0 -1135.7 2271.4 1843
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5459 -0.9659 0.4869 0.7146 1.8166
##
## Random effects:
## Groups Name Variance Std.Dev.
## sub (Intercept) 0.3859 0.6212
## Number of obs: 1848, groups: sub, 153
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.6447 0.1625 10.119 < 2e-16 ***
## as.factor(shortest)3 -0.6342 0.2012 -3.152 0.00162 **
## as.factor(shortest)4 -0.9993 0.1765 -5.663 1.49e-08 ***
## as.factor(shortest)5 -1.5484 0.1759 -8.802 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) as.()3 as.()4
## as.fctr(s)3 -0.718
## as.fctr(s)4 -0.824 0.659
## as.fctr(s)5 -0.835 0.663 0.761
### SWITCHER ###
# raw_Switcher produces a dataframe wherein...
# testtype is the main IV of interest, where the different codes indicate the different rounds of the test (1=2-feature, 2=3-feature, 3=3-feature random)
# numcues tells you how many stimuli were on the screen
# numerr is how many errors were made
# matchlast is how many times the error matched the previous feature
# matchany is how many times the error matched on any feature
# tooslow is how often the response was
# waittime is how long they took before making their first move.
# perftime is how long they took once they made their first move to complete the round
# medtime is the median RT in ms for each response (i.e., after the first)
# Of greatest interest are two DVS: perftime and numerr (how long they took and how many mistakes they made. Matchlast records how many times did an error match the previous rule, which is sort of like a perseveration error found in many tasks
# difference score between two different types
# summarize data by subject, testtype
data_Switcher <- raw_Switcher
data_Switcher$subnum[data_Switcher$subnum==1] <- 8076
data_Switcher$subnum[data_Switcher$subnum==7] <- 8001
data_Switcher$subnum[data_Switcher$subnum==9] <- 8002
data_Switcher$subnum[data_Switcher$subnum==4] <- 8003
data_Switcher$subnum[data_Switcher$subnum==11] <- 8114
data_Switcher$subnum[data_Switcher$subnum==21] <- 8021
data_Switcher$subnum[data_Switcher$subnum==29] <- 8036
data_Switcher$subnum[data_Switcher$subnum==44] <- 8054
data_Switcher$subnum[data_Switcher$subnum==50] <- 8064
data_Switcher$subnum[data_Switcher$subnum==84] <- 8124
data_Switcher$subnum[data_Switcher$subnum==86] <- 8125
data_Switcher$subnum[data_Switcher$subnum==87] <- 8126
data_Switcher_type <- with(data_Switcher, aggregate(list(numerr, perftime, tooslow),
list(testtype),
mean, na.rm=TRUE))
colnames(data_Switcher_type) <- c("switch_type", "switch_err", "switch_rt", "switch_slow")
switcherTypeErr <- ggplot(data_Switcher_type, aes(x=as.factor(switch_type), y=switch_err)) +
geom_bar(stat="identity", width=0.4) +
ggtitle("Number of errors by trial type (1=2-feature, 2=3-feature, 3=3-feature random)") +
ylab("Average number of errors") +
xlab("Trial type") +
theme_few()
switcherTypeErr
switcherTypeRT <- ggplot(data_Switcher_type, aes(x=as.factor(switch_type), y=switch_rt)) +
geom_bar(stat="identity", width=0.4) +
ggtitle("Response time by trial type (1=2-feature, 2=3-feature, 3=3-feature random)") +
ylab("Response time") +
xlab("Trial type") +
theme_few()
switcherTypeRT
switcherTypeSlow <- ggplot(data_Switcher_type, aes(x=as.factor(switch_type), y=switch_slow)) +
geom_bar(stat="identity", width=0.4) +
ggtitle("Slow responses by trial type (1=2-feature, 2=3-feature, 3=3-feature random)") +
ylab("# of too-slow responses") +
xlab("Trial type") +
theme_few()
switcherTypeSlow
data_Switcher_sub <- with(data_Switcher, aggregate(list(numerr, perftime, tooslow),
list(subNum, testtype),
mean, na.rm=TRUE))
colnames(data_Switcher_sub) <- c("subject", "switch_type", "switch_err", "switch_rt", "switch_slow")
data_Switcher_melt <- melt(data_Switcher_sub, id.vars=c("subject", "switch_type"))
data_Switcher_cast <- dcast(data_Switcher_melt, subject ~ switch_type + variable)
colnames(data_Switcher_cast) <- c("subject", "switch_err_1", "switch_rt_1", "switch_slow_1",
"switch_err_2", "switch_rt_2", "switch_slow_2",
"switch_err_3", "switch_rt_3", "switch_slow_3")
data_Switcher_cast$switchEff <- data_Switcher_cast$switch_rt_3 - data_Switcher_cast$switch_rt_2
hist(data_Switcher_cast$switchEff)
### COMBINE ALL INTO ONE ###
data_All <- merge(data_Flanker_trim_cast, data_TOL_success, by="subject", all=TRUE)
data_All <- merge(data_All, data_Switcher_cast, by="subject", all=TRUE)
# run correlations on main measures across tests
flank.TOL.cor <- ggplot(data_All, aes(x=flank_eff, y=TOL_acc)) +
geom_point() +
geom_smooth(method="lm", color="purple") +
ggtitle("Correlation between flanker effect and Tower of London overall accuracy") +
xlab("Flanker effect (Incongruent - Congruent RT)") +
ylab("Tower of London overall accuracy") +
theme_few()
flank.TOL.cor
cor.test(data_All$flank_eff, data_All$TOL_acc)
##
## Pearson's product-moment correlation
##
## data: data_All$flank_eff and data_All$TOL_acc
## t = -0.43796, df = 144, p-value = 0.6621
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.197749 0.126727
## sample estimates:
## cor
## -0.03647226
flank.TOLfirst.cor <- ggplot(data_All, aes(x=flank_eff, y=TOL_first)) +
geom_point() +
geom_smooth(method="lm", color="purple") +
ggtitle("Correlation between flanker effect and Tower of London first move time") +
xlab("Flanker effect (Incongruent - Congruent RT)") +
ylab("Tower of London overall first move time") +
theme_few()
flank.TOLfirst.cor
flank.TOLtime.cor <- ggplot(data_All, aes(x=flank_eff, y=TOL_time)) +
geom_point() +
geom_smooth(method="lm", color="purple") +
ggtitle("Correlation between flanker effect and Tower of London response time") +
xlab("Flanker effect (Incongruent - Congruent RT)") +
ylab("Tower of London overall respnose time") +
theme_few()
flank.TOLtime.cor
cor.test(data_All$flank_eff, data_All$TOL_time)
##
## Pearson's product-moment correlation
##
## data: data_All$flank_eff and data_All$TOL_time
## t = 0.24055, df = 144, p-value = 0.8102
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1428720 0.1818977
## sample estimates:
## cor
## 0.02004152
flank.Switch.cor <- ggplot(data_All, aes(x=flank_eff, y=switchEff)) +
geom_point() +
geom_smooth(method="lm", color="purple") +
ggtitle("Correlation between flanker effect and switcher effect") +
xlab("Flanker effect (Incongruent - Congruent RT)") +
ylab("Switcher effect (Predictable RT - Random RT)") +
theme_few()
flank.Switch.cor
cor.test(data_All$flank_eff, data_All$switchEff)
##
## Pearson's product-moment correlation
##
## data: data_All$flank_eff and data_All$switchEff
## t = 1.2224, df = 136, p-value = 0.2237
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.06396889 0.26670890
## sample estimates:
## cor
## 0.1042503
TOL.Switch.cor <- ggplot(data_All, aes(x=TOL_acc, y=switchEff)) +
geom_point() +
geom_smooth(method="lm", color="purple") +
ggtitle("Correlation between Tower of London accuracy and switcher effect") +
xlab("Tower of London overall accuracy") +
ylab("Switcher effect (Predictable RT - Random RT)") +
theme_few()
TOL.Switch.cor
cor.test(data_All$TOL_acc, data_All$switchEff)
##
## Pearson's product-moment correlation
##
## data: data_All$TOL_acc and data_All$switchEff
## t = 0.41847, df = 141, p-value = 0.6762
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1296790 0.1982219
## sample estimates:
## cor
## 0.03521926
# center all key variables
# first convert to tibble
colnames(data_All)[1] <- "Subject"
rt_wide <- read.csv("/Users/Kayleigh/dissertation/DataAnalysis/Exp2/CatLearning/rt_standardized.csv")
data_All <- merge(data_All, rt_wide, by="Subject")
data_All <- as_tibble(data_All)
data_All$flanker_cent <- scale(data_All$flank_eff)
data_All$TOL_acc_cent <- scale(data_All$TOL_acc)
data_All$TOL_time_cent <- scale(data_All$TOL_time)
data_All$TOL_first_cent <- scale(data_All$TOL_first)
data_All$switch_cent <- scale(data_All$switchEff)
dplyr::filter(data_All, System=="hypothesis-testing") %>%
ggplot(. , aes(x=flanker_cent, y=ashby.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="associative") %>%
ggplot(. , aes(x=flanker_cent, y=ashby.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="hypothesis-testing") %>%
ggplot(. , aes(x=TOL_acc_cent, y=ashby.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="associative") %>%
ggplot(. , aes(x=TOL_acc_cent, y=ashby.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="hypothesis-testing") %>%
ggplot(. , aes(x=TOL_time_cent, y=ashby.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="associative") %>%
ggplot(. , aes(x=TOL_time_cent, y=ashby.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="hypothesis-testing") %>%
ggplot(. , aes(x=TOL_first_cent, y=ashby.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="associative") %>%
ggplot(. , aes(x=TOL_first_cent, y=ashby.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="hypothesis-testing") %>%
ggplot(. , aes(x=switch_cent, y=ashby.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="associative") %>%
ggplot(. , aes(x=switch_cent, y=ashby.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="hypothesis-testing") %>%
ggplot(. , aes(x=flanker_cent, y=sloutsky.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="associative") %>%
ggplot(. , aes(x=flanker_cent, y=sloutsky.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="hypothesis-testing") %>%
ggplot(. , aes(x=TOL_acc_cent, y=sloutsky.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="associative") %>%
ggplot(. , aes(x=TOL_acc_cent, y=sloutsky.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="hypothesis-testing") %>%
ggplot(. , aes(x=TOL_time_cent, y=sloutsky.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="associative") %>%
ggplot(. , aes(x=TOL_time_cent, y=sloutsky.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="hypothesis-testing") %>%
ggplot(. , aes(x=TOL_first_cent, y=sloutsky.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="associative") %>%
ggplot(. , aes(x=TOL_first_cent, y=sloutsky.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="hypothesis-testing") %>%
ggplot(. , aes(x=switch_cent, y=sloutsky.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="associative") %>%
ggplot(. , aes(x=switch_cent, y=sloutsky.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="hypothesis-testing") %>%
ggplot(. , aes(x=flanker_cent, y=tt.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="associative") %>%
ggplot(. , aes(x=flanker_cent, y=tt.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="hypothesis-testing") %>%
ggplot(. , aes(x=TOL_acc_cent, y=tt.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="associative") %>%
ggplot(. , aes(x=TOL_acc_cent, y=tt.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="hypothesis-testing") %>%
ggplot(. , aes(x=TOL_time_cent, y=tt.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="associative") %>%
ggplot(. , aes(x=TOL_time_cent, y=tt.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="hypothesis-testing") %>%
ggplot(. , aes(x=TOL_first_cent, y=tt.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="associative") %>%
ggplot(. , aes(x=TOL_first_cent, y=tt.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="hypothesis-testing") %>%
ggplot(. , aes(x=switch_cent, y=tt.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
dplyr::filter(data_All, System=="associative") %>%
ggplot(. , aes(x=switch_cent, y=tt.t)) +
geom_point() +
geom_smooth(method="lm", se=FALSE)
# ashby models
ashby.mod.0 <- lmer(ashby.t ~ (1|Subject), data_All)
summary(ashby.mod.0)
## Linear mixed model fit by REML ['lmerMod']
## Formula: ashby.t ~ (1 | Subject)
## Data: data_All
##
## REML criterion at convergence: 564.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.43902 -0.65363 -0.05137 0.71451 2.09603
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.3626 0.6022
## Residual 0.6268 0.7917
## Number of obs: 204, groups: Subject, 102
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -0.01112 0.08141 -0.137
ashby.mod.1 <- lmer(ashby.t ~ System +
(1|Subject), data_All)
summary(ashby.mod.1)
## Linear mixed model fit by REML ['lmerMod']
## Formula: ashby.t ~ System + (1 | Subject)
## Data: data_All
##
## REML criterion at convergence: 501.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3199 -0.4637 0.0354 0.5205 2.2489
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.5104 0.7144
## Residual 0.3311 0.5754
## Number of obs: 204, groups: Subject, 102
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.37546 0.09083 4.133
## Systemhypothesis-testing -0.77315 0.08058 -9.595
##
## Correlation of Fixed Effects:
## (Intr)
## Systmhypth- -0.444
ashby.mod.2 <- lmer(ashby.t ~ System +
flanker_cent + switch_cent + TOL_acc_cent + TOL_time_cent + TOL_first_cent +
(1|Subject), data_All)
summary(ashby.mod.2)
## Linear mixed model fit by REML ['lmerMod']
## Formula: ashby.t ~ System + flanker_cent + switch_cent + TOL_acc_cent +
## TOL_time_cent + TOL_first_cent + (1 | Subject)
## Data: data_All
##
## REML criterion at convergence: 443.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4613 -0.4589 0.0504 0.5249 2.0237
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.4135 0.6431
## Residual 0.3219 0.5673
## Number of obs: 184, groups: Subject, 92
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.35628 0.08970 3.972
## Systemhypothesis-testing -0.78009 0.08365 -9.326
## flanker_cent 0.11683 0.08191 1.426
## switch_cent 0.04798 0.07958 0.603
## TOL_acc_cent 0.17282 0.10080 1.715
## TOL_time_cent 0.48757 0.28061 1.738
## TOL_first_cent -0.24566 0.28699 -0.856
##
## Correlation of Fixed Effects:
## (Intr) Systm- flnkr_ swtch_ TOL_c_ TOL_t_
## Systmhypth- -0.466
## flanker_cnt 0.036 0.000
## switch_cent 0.003 0.000 -0.106
## TOL_acc_cnt -0.053 0.000 0.083 0.015
## TOL_tim_cnt -0.043 0.000 -0.002 0.039 0.530
## TOL_frst_cn 0.033 0.000 0.008 -0.065 -0.562 -0.960
ashby.mod.3 <- lmer(ashby.t ~ System +
flanker_cent + switch_cent + TOL_acc_cent +
(System:flanker_cent) + (System:switch_cent) + (System:TOL_acc_cent) +
(1|Subject), data_All)
summary(ashby.mod.3)
## Linear mixed model fit by REML ['lmerMod']
## Formula: ashby.t ~ System + flanker_cent + switch_cent + TOL_acc_cent +
## (System:flanker_cent) + (System:switch_cent) + (System:TOL_acc_cent) +
## (1 | Subject)
## Data: data_All
##
## REML criterion at convergence: 457
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3151 -0.5224 0.0146 0.5346 2.1022
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.4754 0.6895
## Residual 0.3226 0.5679
## Number of obs: 184, groups: Subject, 92
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.370826 0.093356 3.972
## Systemhypothesis-testing -0.780202 0.083938 -9.295
## flanker_cent 0.129523 0.096507 1.342
## switch_cent -0.003216 0.093242 -0.034
## TOL_acc_cent 0.144230 0.098164 1.469
## Systemhypothesis-testing:flanker_cent -0.036312 0.086772 -0.418
## Systemhypothesis-testing:switch_cent 0.138661 0.083836 1.654
## Systemhypothesis-testing:TOL_acc_cent -0.015885 0.088262 -0.180
##
## Correlation of Fixed Effects:
## (Intr) Systm- flnkr_ swtch_ TOL_c_
## Systmhypth- -0.450
## flanker_cnt 0.042 -0.019
## switch_cent 0.002 -0.001 -0.104
## TOL_acc_cnt -0.049 0.022 0.107 -0.030
## Systmhypthss-tstng:f_ -0.019 0.042 -0.450 0.047 -0.048
## Systmhypthss-tstng:s_ -0.001 0.002 0.047 -0.450 0.014
## Syst-:TOL__ 0.022 -0.049 -0.048 0.014 -0.450
## Systmhypthss-tstng:f_ Systmhypthss-tstng:s_
## Systmhypth-
## flanker_cnt
## switch_cent
## TOL_acc_cnt
## Systmhypthss-tstng:f_
## Systmhypthss-tstng:s_ -0.104
## Syst-:TOL__ 0.107 -0.030
# sloutsky models
sloutsky.mod.0 <- lmer(sloutsky.t ~ (1|Subject), data_All)
summary(sloutsky.mod.0)
## Linear mixed model fit by REML ['lmerMod']
## Formula: sloutsky.t ~ (1 | Subject)
## Data: data_All
##
## REML criterion at convergence: 572.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.44707 -0.64353 -0.00053 0.59277 2.09420
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.2985 0.5464
## Residual 0.7058 0.8401
## Number of obs: 204, groups: Subject, 102
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -0.007642 0.079914 -0.096
sloutsky.mod.1 <- lmer(sloutsky.t ~ System +
(1|Subject), data_All)
summary(sloutsky.mod.1)
## Linear mixed model fit by REML ['lmerMod']
## Formula: sloutsky.t ~ System + (1 | Subject)
## Data: data_All
##
## REML criterion at convergence: 543.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.34317 -0.58589 -0.00589 0.54926 1.94749
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.3912 0.6255
## Residual 0.5203 0.7213
## Number of obs: 204, groups: Subject, 102
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.30102 0.09453 3.184
## Systemhypothesis-testing -0.61733 0.10101 -6.112
##
## Correlation of Fixed Effects:
## (Intr)
## Systmhypth- -0.534
sloutsky.mod.2 <- lmer(sloutsky.t ~ System +
flanker_cent + switch_cent + TOL_acc_cent + TOL_time_cent + TOL_first_cent +
(1|Subject), data_All)
summary(sloutsky.mod.2)
## Linear mixed model fit by REML ['lmerMod']
## Formula:
## sloutsky.t ~ System + flanker_cent + switch_cent + TOL_acc_cent +
## TOL_time_cent + TOL_first_cent + (1 | Subject)
## Data: data_All
##
## REML criterion at convergence: 498.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.46401 -0.53755 -0.00954 0.54047 2.15312
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.3774 0.6144
## Residual 0.5319 0.7293
## Number of obs: 184, groups: Subject, 92
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.26376 0.09971 2.645
## Systemhypothesis-testing -0.60135 0.10753 -5.592
## flanker_cent -0.08698 0.08668 -1.004
## switch_cent 0.03774 0.08422 0.448
## TOL_acc_cent 0.08036 0.10667 0.753
## TOL_time_cent -0.03625 0.29696 -0.122
## TOL_first_cent 0.27882 0.30372 0.918
##
## Correlation of Fixed Effects:
## (Intr) Systm- flnkr_ swtch_ TOL_c_ TOL_t_
## Systmhypth- -0.539
## flanker_cnt 0.035 0.000
## switch_cent 0.003 0.000 -0.106
## TOL_acc_cnt -0.051 0.000 0.083 0.015
## TOL_tim_cnt -0.041 0.000 -0.002 0.039 0.530
## TOL_frst_cn 0.032 0.000 0.008 -0.065 -0.562 -0.960
sloutsky.mod.3 <- lmer(sloutsky.t ~ System +
flanker_cent + switch_cent + TOL_acc_cent +
(System:flanker_cent) + (System:switch_cent) + (System:TOL_acc_cent) +
(1|Subject), data_All)
summary(sloutsky.mod.3)
## Linear mixed model fit by REML ['lmerMod']
## Formula:
## sloutsky.t ~ System + flanker_cent + switch_cent + TOL_acc_cent +
## (System:flanker_cent) + (System:switch_cent) + (System:TOL_acc_cent) +
## (1 | Subject)
## Data: data_All
##
## REML criterion at convergence: 500
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.34263 -0.55665 0.05163 0.46018 2.02046
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.4467 0.6683
## Residual 0.4853 0.6966
## Number of obs: 184, groups: Subject, 92
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.26491 0.10089 2.626
## Systemhypothesis-testing -0.58850 0.10296 -5.716
## flanker_cent -0.03825 0.10429 -0.367
## switch_cent 0.09382 0.10076 0.931
## TOL_acc_cent 0.32268 0.10608 3.042
## Systemhypothesis-testing:flanker_cent -0.10941 0.10643 -1.028
## Systemhypothesis-testing:switch_cent -0.06245 0.10283 -0.607
## Systemhypothesis-testing:TOL_acc_cent -0.35355 0.10826 -3.266
##
## Correlation of Fixed Effects:
## (Intr) Systm- flnkr_ swtch_ TOL_c_
## Systmhypth- -0.510
## flanker_cnt 0.042 -0.021
## switch_cent 0.002 -0.001 -0.104
## TOL_acc_cnt -0.049 0.025 0.107 -0.030
## Systmhypthss-tstng:f_ -0.021 0.042 -0.510 0.053 -0.055
## Systmhypthss-tstng:s_ -0.001 0.002 0.053 -0.510 0.015
## Syst-:TOL__ 0.025 -0.049 -0.055 0.015 -0.510
## Systmhypthss-tstng:f_ Systmhypthss-tstng:s_
## Systmhypth-
## flanker_cnt
## switch_cent
## TOL_acc_cnt
## Systmhypthss-tstng:f_
## Systmhypthss-tstng:s_ -0.104
## Syst-:TOL__ 0.107 -0.030
# tax-them models
tt.mod.0 <- lmer(tt.t ~ (1|Subject), data_All)
summary(tt.mod.0)
## Linear mixed model fit by REML ['lmerMod']
## Formula: tt.t ~ (1 | Subject)
## Data: data_All
##
## REML criterion at convergence: 531.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.07819 -0.43346 0.02957 0.53689 2.14763
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.5826 0.7633
## Residual 0.3956 0.6290
## Number of obs: 204, groups: Subject, 102
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -0.01809 0.08747 -0.207
tt.mod.1 <- lmer(tt.t ~ System +
(1|Subject), data_All)
summary(tt.mod.1)
## Linear mixed model fit by REML ['lmerMod']
## Formula: tt.t ~ System + (1 | Subject)
## Data: data_All
##
## REML criterion at convergence: 523.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.03465 -0.50172 0.02106 0.46073 2.43003
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.6018 0.7758
## Residual 0.3572 0.5977
## Number of obs: 204, groups: Subject, 102
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.12670 0.09697 1.307
## Systemhypothesis-testing -0.28959 0.08369 -3.460
##
## Correlation of Fixed Effects:
## (Intr)
## Systmhypth- -0.432
tt.mod.2 <- lmer(tt.t ~ System +
flanker_cent + switch_cent + TOL_acc_cent + TOL_time_cent + TOL_first_cent +
(1|Subject), data_All)
summary(tt.mod.2)
## Linear mixed model fit by REML ['lmerMod']
## Formula:
## tt.t ~ System + flanker_cent + switch_cent + TOL_acc_cent + TOL_time_cent +
## TOL_first_cent + (1 | Subject)
## Data: data_All
##
## REML criterion at convergence: 484.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.22777 -0.47226 0.02059 0.46617 2.31665
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.5942 0.7708
## Residual 0.3760 0.6132
## Number of obs: 184, groups: Subject, 92
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.11523 0.10304 1.118
## Systemhypothesis-testing -0.30414 0.09041 -3.364
## flanker_cent -0.01875 0.09557 -0.196
## switch_cent 0.10202 0.09286 1.099
## TOL_acc_cent -0.01612 0.11762 -0.137
## TOL_time_cent 0.30345 0.32743 0.927
## TOL_first_cent -0.14683 0.33488 -0.438
##
## Correlation of Fixed Effects:
## (Intr) Systm- flnkr_ swtch_ TOL_c_ TOL_t_
## Systmhypth- -0.439
## flanker_cnt 0.037 0.000
## switch_cent 0.003 0.000 -0.106
## TOL_acc_cnt -0.054 0.000 0.083 0.015
## TOL_tim_cnt -0.044 0.000 -0.002 0.039 0.530
## TOL_frst_cn 0.034 0.000 0.008 -0.065 -0.562 -0.960
tt.mod.3 <- lmer(tt.t ~ System +
flanker_cent + switch_cent + TOL_acc_cent +
(System:flanker_cent) + (System:switch_cent) + (System:TOL_acc_cent) +
(1|Subject), data_All)
summary(tt.mod.3)
## Linear mixed model fit by REML ['lmerMod']
## Formula:
## tt.t ~ System + flanker_cent + switch_cent + TOL_acc_cent + (System:flanker_cent) +
## (System:switch_cent) + (System:TOL_acc_cent) + (1 | Subject)
## Data: data_All
##
## REML criterion at convergence: 486.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.83323 -0.47297 0.02074 0.45537 2.42570
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.6148 0.7841
## Residual 0.3613 0.6011
## Number of obs: 184, groups: Subject, 92
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.11676 0.10325 1.131
## Systemhypothesis-testing -0.28875 0.08883 -3.250
## flanker_cent -0.09827 0.10673 -0.921
## switch_cent 0.08612 0.10312 0.835
## TOL_acc_cent 0.03979 0.10856 0.367
## Systemhypothesis-testing:flanker_cent 0.15195 0.09183 1.655
## Systemhypothesis-testing:switch_cent 0.05558 0.08872 0.626
## Systemhypothesis-testing:TOL_acc_cent -0.15437 0.09341 -1.653
##
## Correlation of Fixed Effects:
## (Intr) Systm- flnkr_ swtch_ TOL_c_
## Systmhypth- -0.430
## flanker_cnt 0.042 -0.018
## switch_cent 0.002 -0.001 -0.104
## TOL_acc_cnt -0.049 0.021 0.107 -0.030
## Systmhypthss-tstng:f_ -0.018 0.042 -0.430 0.045 -0.046
## Systmhypthss-tstng:s_ -0.001 0.002 0.045 -0.430 0.013
## Syst-:TOL__ 0.021 -0.049 -0.046 0.013 -0.430
## Systmhypthss-tstng:f_ Systmhypthss-tstng:s_
## Systmhypth-
## flanker_cnt
## switch_cent
## TOL_acc_cnt
## Systmhypthss-tstng:f_
## Systmhypthss-tstng:s_ -0.104
## Syst-:TOL__ 0.107 -0.030